#include <darknet/darknet.h>

#include <stdio.h>
#include <time.h>
#include <assert.h>

#include <darknet/network.h>
#include <darknet/image.h>
#include <darknet/data.h>
#include <darknet/utils.h>
#include <darknet/blas.h>

#include <darknet/crop_layer.h>
#include <darknet/connected_layer.h>
#include <darknet/gru_layer.h>
#include <darknet/rnn_layer.h>
#include <darknet/crnn_layer.h>
#include <darknet/conv_lstm_layer.h>
#include <darknet/local_layer.h>
#include <darknet/convolutional_layer.h>
#include <darknet/activation_layer.h>
#include <darknet/detection_layer.h>
#include <darknet/region_layer.h>
#include <darknet/normalization_layer.h>
#include <darknet/batchnorm_layer.h>
#include <darknet/maxpool_layer.h>
#include <darknet/reorg_layer.h>
#include <darknet/reorg_old_layer.h>
#include <darknet/avgpool_layer.h>
#include <darknet/cost_layer.h>
#include <darknet/softmax_layer.h>
#include <darknet/dropout_layer.h>
#include <darknet/route_layer.h>
#include <darknet/shortcut_layer.h>
#include <darknet/scale_channels_layer.h>
#include <darknet/yolo_layer.h>
#include <darknet/upsample_layer.h>
#include <darknet/parser.h>

load_args get_base_args(network *net)
{
    load_args args = { 0 };
    args.w = net->w;
    args.h = net->h;
    args.size = net->w;

    args.min = net->min_crop;
    args.max = net->max_crop;
    args.angle = net->angle;
    args.aspect = net->aspect;
    args.exposure = net->exposure;
    args.center = net->center;
    args.saturation = net->saturation;
    args.hue = net->hue;
    return args;
}

int get_current_batch(network net)
{
    int batch_num = (*net.seen) / (net.batch * net.subdivisions);
    return batch_num;
}

void reset_momentum(network net)
{
    if (net.momentum == 0)
    {
        return;
    }

    net.learning_rate = 0;
    net.momentum = 0;
    net.decay = 0;
#ifdef GPU
    //if(net.gpu_index >= 0) update_network_gpu(net);
#endif
}

void reset_network_state(network *net, int b)
{
    int i;

    for (i = 0; i < net->n; ++i)
    {
#ifdef GPU
        layer l = net->layers[i];

        if (l.state_gpu)
        {
            fill_ongpu(l.outputs, 0, l.state_gpu + l.outputs * b, 1);
        }

        if (l.h_gpu)
        {
            fill_ongpu(l.outputs, 0, l.h_gpu + l.outputs * b, 1);
        }

#endif
    }
}

void reset_rnn(network *net)
{
    reset_network_state(net, 0);
}

float get_current_seq_subdivisions(network net)
{
    int sequence_subdivisions = net.init_sequential_subdivisions;

    if (net.num_steps > 0)
    {
        int batch_num = get_current_batch(net);
        int i;

        for (i = 0; i < net.num_steps; ++i)
        {
            if (net.steps[i] > batch_num)
            {
                break;
            }

            sequence_subdivisions *= net.seq_scales[i];
        }
    }

    if (sequence_subdivisions < 1)
    {
        sequence_subdivisions = 1;
    }

    if (sequence_subdivisions > net.subdivisions)
    {
        sequence_subdivisions = net.subdivisions;
    }

    return sequence_subdivisions;
}

int get_sequence_value(network net)
{
    int sequence = 1;

    if (net.sequential_subdivisions != 0)
    {
        sequence = net.subdivisions / net.sequential_subdivisions;
    }

    if (sequence < 1)
    {
        sequence = 1;
    }

    return sequence;
}

float get_current_rate(network net)
{
    int batch_num = get_current_batch(net);
    int i;
    float rate;

    if (batch_num < net.burn_in)
    {
        return net.learning_rate * pow((float)batch_num / net.burn_in, net.power);
    }

    switch (net.policy)
    {
        case CONSTANT:
            return net.learning_rate;

        case STEP:
            return net.learning_rate * pow(net.scale, batch_num / net.step);

        case STEPS:
            rate = net.learning_rate;

            for (i = 0; i < net.num_steps; ++i)
            {
                if (net.steps[i] > batch_num)
                {
                    return rate;
                }

                rate *= net.scales[i];
                //if(net.steps[i] > batch_num - 1 && net.scales[i] > 1) reset_momentum(net);
            }

            return rate;

        case EXP:
            return net.learning_rate * pow(net.gamma, batch_num);

        case POLY:
            return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);

        //if (batch_num < net.burn_in) return net.learning_rate * pow((float)batch_num / net.burn_in, net.power);
        //return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
        case RANDOM:
            return net.learning_rate * pow(rand_uniform(0, 1), net.power);

        case SIG:
            return net.learning_rate * (1. / (1. + exp(net.gamma * (batch_num - net.step))));

        case SGDR:
        {
            int last_iteration_start = 0;
            int cycle_size = net.batches_per_cycle;

            while ((last_iteration_start + cycle_size) < batch_num)
            {
                last_iteration_start += cycle_size;
                cycle_size *= net.batches_cycle_mult;
            }

            rate = net.learning_rate_min +
                   0.5 * (net.learning_rate - net.learning_rate_min)
                   * (1. + cos((float)(batch_num - last_iteration_start) * 3.14159265 / cycle_size));

            return rate;
        }

        default:
            fprintf(stderr, "Policy is weird!\n");
            return net.learning_rate;
    }
}

char *get_layer_string(LAYER_TYPE a)
{
    switch (a)
    {
        case CONVOLUTIONAL:
            return "convolutional";

        case ACTIVE:
            return "activation";

        case LOCAL:
            return "local";

        case DECONVOLUTIONAL:
            return "deconvolutional";

        case CONNECTED:
            return "connected";

        case RNN:
            return "rnn";

        case GRU:
            return "gru";

        case LSTM:
            return "lstm";

        case CRNN:
            return "crnn";

        case MAXPOOL:
            return "maxpool";

        case REORG:
            return "reorg";

        case AVGPOOL:
            return "avgpool";

        case SOFTMAX:
            return "softmax";

        case DETECTION:
            return "detection";

        case REGION:
            return "region";

        case DROPOUT:
            return "dropout";

        case CROP:
            return "crop";

        case COST:
            return "cost";

        case ROUTE:
            return "route";

        case SHORTCUT:
            return "shortcut";

        case NORMALIZATION:
            return "normalization";

        case BATCHNORM:
            return "batchnorm";

        default:
            break;
    }

    return "none";
}

network make_network(int n)
{
    network net = {0};
    net.n = n;
    net.layers = (layer *)calloc(net.n, sizeof(layer));
    net.seen = (uint64_t *)calloc(1, sizeof(uint64_t));
#ifdef GPU
    net.input_gpu = (float **)calloc(1, sizeof(float *));
    net.truth_gpu = (float **)calloc(1, sizeof(float *));

    net.input16_gpu = (float **)calloc(1, sizeof(float *));
    net.output16_gpu = (float **)calloc(1, sizeof(float *));
    net.max_input16_size = (size_t *)calloc(1, sizeof(size_t));
    net.max_output16_size = (size_t *)calloc(1, sizeof(size_t));
#endif
    return net;
}

void forward_network(network net, network_state state)
{
    state.workspace = net.workspace;
    int i;

    for (i = 0; i < net.n; ++i)
    {
        state.index = i;
        layer l = net.layers[i];

        if (l.delta && state.train)
        {
            scal_cpu(l.outputs * l.batch, 0, l.delta, 1);
        }

        //double time = get_time_point();
        l.forward(l, state);
        //printf("%d - Predicted in %lf milli-seconds.\n", i, ((double)get_time_point() - time) / 1000);
        state.input = l.output;
    }
}

void update_network(network net)
{
    int i;
    int update_batch = net.batch * net.subdivisions;
    float rate = get_current_rate(net);

    for (i = 0; i < net.n; ++i)
    {
        layer l = net.layers[i];

        if (l.update)
        {
            l.update(l, update_batch, rate, net.momentum, net.decay);
        }
    }
}

float *get_network_output(network net)
{
#ifdef GPU

    if (gpu_index >= 0)
    {
        return get_network_output_gpu(net);
    }

#endif
    int i;

    for (i = net.n - 1; i > 0; --i) if (net.layers[i].type != COST)
        {
            break;
        }

    return net.layers[i].output;
}

float get_network_cost(network net)
{
    int i;
    float sum = 0;
    int count = 0;

    for (i = 0; i < net.n; ++i)
    {
        if (net.layers[i].cost)
        {
            sum += net.layers[i].cost[0];
            ++count;
        }
    }

    return sum / count;
}

int get_predicted_class_network(network net)
{
    float *out = get_network_output(net);
    int k = get_network_output_size(net);
    return max_index(out, k);
}

void backward_network(network net, network_state state)
{
    int i;
    float *original_input = state.input;
    float *original_delta = state.delta;
    state.workspace = net.workspace;

    for (i = net.n - 1; i >= 0; --i)
    {
        state.index = i;

        if (i == 0)
        {
            state.input = original_input;
            state.delta = original_delta;
        }
        else
        {
            layer prev = net.layers[i - 1];
            state.input = prev.output;
            state.delta = prev.delta;
        }

        layer l = net.layers[i];

        if (l.stopbackward)
        {
            break;
        }

        if (l.onlyforward)
        {
            continue;
        }

        l.backward(l, state);
    }
}

float train_network_datum(network net, float *x, float *y)
{
#ifdef GPU

    if (gpu_index >= 0)
    {
        return train_network_datum_gpu(net, x, y);
    }

#endif
    network_state state;
    *net.seen += net.batch;
    state.index = 0;
    state.net = net;
    state.input = x;
    state.delta = 0;
    state.truth = y;
    state.train = 1;
    forward_network(net, state);
    backward_network(net, state);
    float error = get_network_cost(net);

    if (((*net.seen) / net.batch) % net.subdivisions == 0)
    {
        update_network(net);
    }

    return error;
}

float train_network_sgd(network net, data d, int n)
{
    int batch = net.batch;
    float *X = (float *)calloc(batch * d.X.cols, sizeof(float));
    float *y = (float *)calloc(batch * d.y.cols, sizeof(float));

    int i;
    float sum = 0;

    for (i = 0; i < n; ++i)
    {
        get_random_batch(d, batch, X, y);
        net.current_subdivision = i;
        float err = train_network_datum(net, X, y);
        sum += err;
    }

    free(X);
    free(y);
    return (float)sum / (n * batch);
}

float train_network(network net, data d)
{
    return train_network_waitkey(net, d, 0);
}

float train_network_waitkey(network net, data d, int wait_key)
{
    assert(d.X.rows % net.batch == 0);
    int batch = net.batch;
    int n = d.X.rows / batch;
    float *X = (float *)calloc(batch * d.X.cols, sizeof(float));
    float *y = (float *)calloc(batch * d.y.cols, sizeof(float));

    int i;
    float sum = 0;

    for (i = 0; i < n; ++i)
    {
        get_next_batch(d, batch, i * batch, X, y);
        net.current_subdivision = i;
        float err = train_network_datum(net, X, y);
        sum += err;

        if (wait_key)
        {
            wait_key_cv(5);
        }
    }

    free(X);
    free(y);
    return (float)sum / (n * batch);
}


float train_network_batch(network net, data d, int n)
{
    int i, j;
    network_state state;
    state.index = 0;
    state.net = net;
    state.train = 1;
    state.delta = 0;
    float sum = 0;
    int batch = 2;

    for (i = 0; i < n; ++i)
    {
        for (j = 0; j < batch; ++j)
        {
            int index = random_gen() % d.X.rows;
            state.input = d.X.vals[index];
            state.truth = d.y.vals[index];
            forward_network(net, state);
            backward_network(net, state);
            sum += get_network_cost(net);
        }

        update_network(net);
    }

    return (float)sum / (n * batch);
}

int recalculate_workspace_size(network *net)
{
#ifdef GPU
    cuda_set_device(net->gpu_index);

    if (gpu_index >= 0)
    {
        cuda_free(net->workspace);
    }

#endif
    int i;
    size_t workspace_size = 0;

    for (i = 0; i < net->n; ++i)
    {
        layer l = net->layers[i];

        //printf(" %d: layer = %d,", i, l.type);
        if (l.type == CONVOLUTIONAL)
        {
            l.workspace_size = get_convolutional_workspace_size(l);
        }
        else if (l.type == CONNECTED)
        {
            l.workspace_size = get_connected_workspace_size(l);
        }

        if (l.workspace_size > workspace_size)
        {
            workspace_size = l.workspace_size;
        }

        net->layers[i] = l;
    }

#ifdef GPU

    if (gpu_index >= 0)
    {
        printf("\n try to allocate additional workspace_size = %1.2f MB \n", (float)workspace_size / 1000000);
        net->workspace = cuda_make_array(0, workspace_size / sizeof(float) + 1);
        printf(" CUDA allocate done! \n");
    }
    else
    {
        free(net->workspace);
        net->workspace = (float *)calloc(1, workspace_size);
    }

#else
    free(net->workspace);
    net->workspace = (float *)calloc(1, workspace_size);
#endif
    //fprintf(stderr, " Done!\n");
    return 0;
}

void set_batch_network(network *net, int b)
{
    net->batch = b;
    int i;

    for (i = 0; i < net->n; ++i)
    {
        net->layers[i].batch = b;

#ifdef CUDNN

        if (net->layers[i].type == CONVOLUTIONAL)
        {
            cudnn_convolutional_setup(net->layers + i, cudnn_fastest);
        }
        else if (net->layers[i].type == MAXPOOL)
        {
            cudnn_maxpool_setup(net->layers + i);
        }

#endif

    }

    recalculate_workspace_size(net); // recalculate workspace size
}

int resize_network(network *net, int w, int h)
{
#ifdef GPU
    cuda_set_device(net->gpu_index);

    if (gpu_index >= 0)
    {
        cuda_free(net->workspace);

        if (net->input_gpu)
        {
            cuda_free(*net->input_gpu);
            *net->input_gpu = 0;
            cuda_free(*net->truth_gpu);
            *net->truth_gpu = 0;
        }

        if (net->input_state_gpu)
        {
            cuda_free(net->input_state_gpu);
        }

        if (net->input_pinned_cpu)
        {
            if (net->input_pinned_cpu_flag)
            {
                cudaFreeHost(net->input_pinned_cpu);
            }
            else
            {
                free(net->input_pinned_cpu);
            }
        }
    }

#endif
    int i;
    //if(w == net->w && h == net->h) return 0;
    net->w = w;
    net->h = h;
    int inputs = 0;
    size_t workspace_size = 0;

    //fprintf(stderr, "Resizing to %d x %d...\n", w, h);
    //fflush(stderr);
    for (i = 0; i < net->n; ++i)
    {
        layer l = net->layers[i];

        //printf(" %d: layer = %d,", i, l.type);
        if (l.type == CONVOLUTIONAL)
        {
            resize_convolutional_layer(&l, w, h);
        }
        else if (l.type == CRNN)
        {
            resize_crnn_layer(&l, w, h);
        }
        else if (l.type == CONV_LSTM)
        {
            resize_conv_lstm_layer(&l, w, h);
        }
        else if (l.type == CROP)
        {
            resize_crop_layer(&l, w, h);
        }
        else if (l.type == MAXPOOL)
        {
            resize_maxpool_layer(&l, w, h);
        }
        else if (l.type == REGION)
        {
            resize_region_layer(&l, w, h);
        }
        else if (l.type == YOLO)
        {
            resize_yolo_layer(&l, w, h);
        }
        else if (l.type == ROUTE)
        {
            resize_route_layer(&l, net);
        }
        else if (l.type == SHORTCUT)
        {
            resize_shortcut_layer(&l, w, h);
        }
        else if (l.type == SCALE_CHANNELS)
        {
            resize_scale_channels_layer(&l, w, h);
        }
        else if (l.type == UPSAMPLE)
        {
            resize_upsample_layer(&l, w, h);
        }
        else if (l.type == REORG)
        {
            resize_reorg_layer(&l, w, h);
        }
        else if (l.type == REORG_OLD)
        {
            resize_reorg_old_layer(&l, w, h);
        }
        else if (l.type == AVGPOOL)
        {
            resize_avgpool_layer(&l, w, h);
        }
        else if (l.type == NORMALIZATION)
        {
            resize_normalization_layer(&l, w, h);
        }
        else if (l.type == COST)
        {
            resize_cost_layer(&l, inputs);
        }
        else
        {
            fprintf(stderr, "Resizing type %d \n", (int)l.type);
            error("Cannot resize this type of layer");
        }

        if (l.workspace_size > workspace_size)
        {
            workspace_size = l.workspace_size;
        }

        inputs = l.outputs;
        net->layers[i] = l;
        w = l.out_w;
        h = l.out_h;

        if (l.type == AVGPOOL)
        {
            break;
        }
    }

#ifdef GPU
    const int size = get_network_input_size(*net) * net->batch;

    if (gpu_index >= 0)
    {
        printf(" try to allocate additional workspace_size = %1.2f MB \n", (float)workspace_size / 1000000);
        net->workspace = cuda_make_array(0, workspace_size / sizeof(float) + 1);
        net->input_state_gpu = cuda_make_array(0, size);

        if (cudaSuccess == cudaHostAlloc(&net->input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped))
        {
            net->input_pinned_cpu_flag = 1;
        }
        else
        {
            cudaGetLastError(); // reset CUDA-error
            net->input_pinned_cpu = (float *)calloc(size, sizeof(float));
            net->input_pinned_cpu_flag = 0;
        }

        printf(" CUDA allocate done! \n");
    }
    else
    {
        free(net->workspace);
        net->workspace = (float *)calloc(1, workspace_size);

        if (!net->input_pinned_cpu_flag)
        {
            net->input_pinned_cpu = (float *)realloc(net->input_pinned_cpu, size * sizeof(float));
        }
    }

#else
    free(net->workspace);
    net->workspace = (float *)calloc(1, workspace_size);
#endif
    //fprintf(stderr, " Done!\n");
    return 0;
}

int get_network_output_size(network net)
{
    int i;

    for (i = net.n - 1; i > 0; --i) if (net.layers[i].type != COST)
        {
            break;
        }

    return net.layers[i].outputs;
}

int get_network_input_size(network net)
{
    return net.layers[0].inputs;
}

detection_layer get_network_detection_layer(network net)
{
    int i;

    for (i = 0; i < net.n; ++i)
    {
        if (net.layers[i].type == DETECTION)
        {
            return net.layers[i];
        }
    }

    fprintf(stderr, "Detection layer not found!!\n");
    detection_layer l = { (LAYER_TYPE)0 };
    return l;
}

image get_network_image_layer(network net, int i)
{
    layer l = net.layers[i];

    if (l.out_w && l.out_h && l.out_c)
    {
        return float_to_image(l.out_w, l.out_h, l.out_c, l.output);
    }

    image def = {0};
    return def;
}

layer *get_network_layer(network *net, int i)
{
    return net->layers + i;
}

image get_network_image(network net)
{
    int i;

    for (i = net.n - 1; i >= 0; --i)
    {
        image m = get_network_image_layer(net, i);

        if (m.h != 0)
        {
            return m;
        }
    }

    image def = {0};
    return def;
}

void visualize_network(network net)
{
    image *prev = 0;
    int i;
    char buff[256];

    for (i = 0; i < net.n; ++i)
    {
        sprintf(buff, "Layer %d", i);
        layer l = net.layers[i];

        if (l.type == CONVOLUTIONAL)
        {
            prev = visualize_convolutional_layer(l, buff, prev);
        }
    }
}

void top_predictions(network net, int k, int *index)
{
    int size = get_network_output_size(net);
    float *out = get_network_output(net);
    top_k(out, size, k, index);
}

// A version of network_predict that uses a pointer for the network
// struct to make the python binding work properly.
float *network_predict_ptr(network *net, float *input)
{
    return network_predict(*net, input);
}

float *network_predict(network net, float *input)
{
#ifdef GPU

    if (gpu_index >= 0)
    {
        return network_predict_gpu(net, input);
    }

#endif

    network_state state;
    state.net = net;
    state.index = 0;
    state.input = input;
    state.truth = 0;
    state.train = 0;
    state.delta = 0;
    forward_network(net, state);
    float *out = get_network_output(net);
    return out;
}

float *network_predict1(network net, float *input)
{
#ifdef GPU

    if (gpu_index >= 0)
    {
        return network_predict_gpu1(net, input);
    }

#endif

    network_state state;
    state.net = net;
    state.index = 0;
    state.input = input;
    state.truth = 0;
    state.train = 0;
    state.delta = 0;
    forward_network(net, state);
    float *out = get_network_output(net);
    return out;
}

int num_detections(network *net, float thresh)
{
    int i;
    int s = 0;

    for (i = 0; i < net->n; ++i)
    {
        layer l = net->layers[i];

        if (l.type == YOLO)
        {
            s += yolo_num_detections(l, thresh);
        }

        if (l.type == DETECTION || l.type == REGION)
        {
            s += l.w * l.h * l.n;
        }
    }

    return s;
}

detection *make_network_boxes(network *net, float thresh, int *num)
{
    layer l = net->layers[net->n - 1];
    int i;
    int nboxes = num_detections(net, thresh);

    if (num)
    {
        *num = nboxes;
    }

    detection *dets = (detection *)calloc(nboxes, sizeof(detection));

    for (i = 0; i < nboxes; ++i)
    {
        dets[i].prob = (float *)calloc(l.classes, sizeof(float));

        if (l.coords > 4)
        {
            dets[i].mask = (float *)calloc(l.coords - 4, sizeof(float));
        }
    }

    return dets;
}


void custom_get_region_detections(layer l, int w, int h, int net_w, int net_h, float thresh, int *map, float hier, int relative, detection *dets, int letter)
{
    box *boxes = (box *)calloc(l.w * l.h * l.n, sizeof(box));
    float **probs = (float **)calloc(l.w * l.h * l.n, sizeof(float *));
    int i, j;

    for (j = 0; j < l.w * l.h * l.n; ++j)
    {
        probs[j] = (float *)calloc(l.classes, sizeof(float));
    }

    get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, map);

    for (j = 0; j < l.w * l.h * l.n; ++j)
    {
        dets[j].classes = l.classes;
        dets[j].bbox = boxes[j];
        dets[j].objectness = 1;

        for (i = 0; i < l.classes; ++i)
        {
            dets[j].prob[i] = probs[j][i];
        }
    }

    free(boxes);
    free_ptrs((void **)probs, l.w * l.h * l.n);

    //correct_region_boxes(dets, l.w*l.h*l.n, w, h, net_w, net_h, relative);
    correct_yolo_boxes(dets, l.w * l.h * l.n, w, h, net_w, net_h, relative, letter);
}

void fill_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, detection *dets, int letter)
{
    int prev_classes = -1;
    int j;

    for (j = 0; j < net->n; ++j)
    {
        layer l = net->layers[j];

        if (l.type == YOLO)
        {
            int count = get_yolo_detections(l, w, h, net->w, net->h, thresh, map, relative, dets, letter);
            dets += count;

            if (prev_classes < 0)
            {
                prev_classes = l.classes;
            }
            else if (prev_classes != l.classes)
            {
                printf(" Error: Different [yolo] layers have different number of classes = %d and %d - check your cfg-file! \n",
                       prev_classes, l.classes);
            }
        }

        if (l.type == REGION)
        {
            custom_get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets, letter);
            //get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets);
            dets += l.w * l.h * l.n;
        }

        if (l.type == DETECTION)
        {
            get_detection_detections(l, w, h, thresh, dets);
            dets += l.w * l.h * l.n;
        }
    }
}

detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter)
{
    detection *dets = make_network_boxes(net, thresh, num);
    fill_network_boxes(net, w, h, thresh, hier, map, relative, dets, letter);
    return dets;
}

void free_detections(detection *dets, int n)
{
    int i;

    for (i = 0; i < n; ++i)
    {
        free(dets[i].prob);

        if (dets[i].mask)
        {
            free(dets[i].mask);
        }
    }

    free(dets);
}

// JSON format:
//{
// "frame_id":8990,
// "objects":[
//  {"class_id":4, "name":"aeroplane", "relative coordinates":{"center_x":0.398831, "center_y":0.630203, "width":0.057455, "height":0.020396}, "confidence":0.793070},
//  {"class_id":14, "name":"bird", "relative coordinates":{"center_x":0.398831, "center_y":0.630203, "width":0.057455, "height":0.020396}, "confidence":0.265497}
// ]
//},

char *detection_to_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, char *filename)
{
    const float thresh = 0.005; // function get_network_boxes() has already filtred dets by actual threshold

    char *send_buf = (char *)calloc(1024, sizeof(char));

    if (filename)
    {
        sprintf(send_buf, "{\n \"frame_id\":%lld, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename);
    }
    else
    {
        sprintf(send_buf, "{\n \"frame_id\":%lld, \n \"objects\": [ \n", frame_id);
    }

    int i, j;
    int class_id = -1;

    for (i = 0; i < nboxes; ++i)
    {
        for (j = 0; j < classes; ++j)
        {
            int show = strncmp(names[j], "dont_show", 9);

            if (dets[i].prob[j] > thresh && show)
            {
                if (class_id != -1)
                {
                    strcat(send_buf, ", \n");
                }

                class_id = j;
                char *buf = (char *)calloc(2048, sizeof(char));
                //sprintf(buf, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f}",
                //    image_id, j, dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h, dets[i].prob[j]);

                sprintf(buf, "  {\"class_id\":%d, \"name\":\"%s\", \"relative_coordinates\":{\"center_x\":%f, \"center_y\":%f, \"width\":%f, \"height\":%f}, \"confidence\":%f}",
                        j, names[j], dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h, dets[i].prob[j]);

                int send_buf_len = strlen(send_buf);
                int buf_len = strlen(buf);
                int total_len = send_buf_len + buf_len + 100;
                send_buf = (char *)realloc(send_buf, total_len * sizeof(char));

                if (!send_buf)
                {
                    return 0;    // exit(-1);
                }

                strcat(send_buf, buf);
                free(buf);
            }
        }
    }

    //strcat(send_buf, "\n ] \n}, \n");
    strcat(send_buf, "\n ] \n}");
    return send_buf;
}


float *network_predict_image(network *net, image im)
{
    //image imr = letterbox_image(im, net->w, net->h);
    float *p;

    if (net->batch != 1)
    {
        set_batch_network(net, 1);
    }

    if (im.w == net->w && im.h == net->h)
    {
        // Input image is the same size as our net, predict on that image
        p = network_predict(*net, im.data);
    }
    else
    {
        // Need to resize image to the desired size for the net
        image imr = resize_image(im, net->w, net->h);
        p = network_predict(*net, imr.data);
        free_image(imr);
    }

    return p;
}

float *network_predict_image_letterbox(network *net, image im)
{
    //image imr = letterbox_image(im, net->w, net->h);
    float *p;

    if (net->batch != 1)
    {
        set_batch_network(net, 1);
    }

    if (im.w == net->w && im.h == net->h)
    {
        // Input image is the same size as our net, predict on that image
        p = network_predict(*net, im.data);
    }
    else
    {
        // Need to resize image to the desired size for the net
        image imr = letterbox_image(im, net->w, net->h);
        p = network_predict(*net, imr.data);
        free_image(imr);
    }

    return p;
}

int network_width(network *net)
{
    return net->w;
}
int network_height(network *net)
{
    return net->h;
}

matrix network_predict_data_multi(network net, data test, int n)
{
    int i, j, b, m;
    int k = get_network_output_size(net);
    matrix pred = make_matrix(test.X.rows, k);
    float *X = (float *)calloc(net.batch * test.X.rows, sizeof(float));

    for (i = 0; i < test.X.rows; i += net.batch)
    {
        for (b = 0; b < net.batch; ++b)
        {
            if (i + b == test.X.rows)
            {
                break;
            }

            memcpy(X + b * test.X.cols, test.X.vals[i + b], test.X.cols * sizeof(float));
        }

        for (m = 0; m < n; ++m)
        {
            float *out = network_predict(net, X);

            for (b = 0; b < net.batch; ++b)
            {
                if (i + b == test.X.rows)
                {
                    break;
                }

                for (j = 0; j < k; ++j)
                {
                    pred.vals[i + b][j] += out[j + b * k] / n;
                }
            }
        }
    }

    free(X);
    return pred;
}

matrix network_predict_data(network net, data test)
{
    int i, j, b;
    int k = get_network_output_size(net);
    matrix pred = make_matrix(test.X.rows, k);
    float *X = (float *)calloc(net.batch * test.X.cols, sizeof(float));

    for (i = 0; i < test.X.rows; i += net.batch)
    {
        for (b = 0; b < net.batch; ++b)
        {
            if (i + b == test.X.rows)
            {
                break;
            }

            memcpy(X + b * test.X.cols, test.X.vals[i + b], test.X.cols * sizeof(float));
        }

        float *out = network_predict(net, X);

        for (b = 0; b < net.batch; ++b)
        {
            if (i + b == test.X.rows)
            {
                break;
            }

            for (j = 0; j < k; ++j)
            {
                pred.vals[i + b][j] = out[j + b * k];
            }
        }
    }

    free(X);
    return pred;
}

void print_network(network net)
{
    int i, j;

    for (i = 0; i < net.n; ++i)
    {
        layer l = net.layers[i];
        float *output = l.output;
        int n = l.outputs;
        float mean = mean_array(output, n);
        float vari = variance_array(output, n);
        fprintf(stderr, "Layer %d - Mean: %f, Variance: %f\n", i, mean, vari);

        if (n > 100)
        {
            n = 100;
        }

        for (j = 0; j < n; ++j)
        {
            fprintf(stderr, "%f, ", output[j]);
        }

        if (n == 100)
        {
            fprintf(stderr, ".....\n");
        }

        fprintf(stderr, "\n");
    }
}

void compare_networks(network n1, network n2, data test)
{
    matrix g1 = network_predict_data(n1, test);
    matrix g2 = network_predict_data(n2, test);
    int i;
    int a, b, c, d;
    a = b = c = d = 0;

    for (i = 0; i < g1.rows; ++i)
    {
        int truth = max_index(test.y.vals[i], test.y.cols);
        int p1 = max_index(g1.vals[i], g1.cols);
        int p2 = max_index(g2.vals[i], g2.cols);

        if (p1 == truth)
        {
            if (p2 == truth)
            {
                ++d;
            }
            else
            {
                ++c;
            }
        }
        else
        {
            if (p2 == truth)
            {
                ++b;
            }
            else
            {
                ++a;
            }
        }
    }

    printf("%5d %5d\n%5d %5d\n", a, b, c, d);
    float num = pow((abs(b - c) - 1.), 2.);
    float den = b + c;
    printf("%f\n", num / den);
}

float network_accuracy(network net, data d)
{
    matrix guess = network_predict_data(net, d);
    float acc = matrix_topk_accuracy(d.y, guess, 1);
    free_matrix(guess);
    return acc;
}

float *network_accuracies(network net, data d, int n)
{
    static float acc[2];
    matrix guess = network_predict_data(net, d);
    acc[0] = matrix_topk_accuracy(d.y, guess, 1);
    acc[1] = matrix_topk_accuracy(d.y, guess, n);
    free_matrix(guess);
    return acc;
}

float network_accuracy_multi(network net, data d, int n)
{
    matrix guess = network_predict_data_multi(net, d, n);
    float acc = matrix_topk_accuracy(d.y, guess, 1);
    free_matrix(guess);
    return acc;
}

void free_network(network net)
{
    int i;

    for (i = 0; i < net.n; ++i)
    {
        free_layer(net.layers[i]);
    }

    free(net.layers);

    free(net.seq_scales);
    free(net.scales);
    free(net.steps);
    free(net.seen);

#ifdef GPU

    if (gpu_index >= 0)
    {
        cuda_free(net.workspace);
    }
    else
    {
        free(net.workspace);
    }

    if (net.input_state_gpu)
    {
        cuda_free(net.input_state_gpu);
    }

    if (net.input_pinned_cpu)     // CPU
    {
        if (net.input_pinned_cpu_flag)
        {
            cudaFreeHost(net.input_pinned_cpu);
        }
        else
        {
            free(net.input_pinned_cpu);
        }
    }

    if (*net.input_gpu)
    {
        cuda_free(*net.input_gpu);
    }

    if (*net.truth_gpu)
    {
        cuda_free(*net.truth_gpu);
    }

    if (net.input_gpu)
    {
        free(net.input_gpu);
    }

    if (net.truth_gpu)
    {
        free(net.truth_gpu);
    }

    if (*net.input16_gpu)
    {
        cuda_free(*net.input16_gpu);
    }

    if (*net.output16_gpu)
    {
        cuda_free(*net.output16_gpu);
    }

    if (net.input16_gpu)
    {
        free(net.input16_gpu);
    }

    if (net.output16_gpu)
    {
        free(net.output16_gpu);
    }

    if (net.max_input16_size)
    {
        free(net.max_input16_size);
    }

    if (net.max_output16_size)
    {
        free(net.max_output16_size);
    }

#else
    free(net.workspace);
#endif
}


void fuse_conv_batchnorm(network net)
{
    int j;

    for (j = 0; j < net.n; ++j)
    {
        layer *l = &net.layers[j];

        if (l->type == CONVOLUTIONAL)
        {
            //printf(" Merges Convolutional-%d and batch_norm \n", j);

            if (l->batch_normalize)
            {
                int f;

                for (f = 0; f < l->n; ++f)
                {
                    //l->biases[f] = l->biases[f] - (double)l->scales[f] * l->rolling_mean[f] / (sqrt((double)l->rolling_variance[f]) + .000001f);
                    l->biases[f] = l->biases[f] - (double)l->scales[f] * l->rolling_mean[f] / (sqrt((double)l->rolling_variance[f] + .000001));

                    const size_t filter_size = l->size * l->size * l->c / l->groups;
                    int i;

                    for (i = 0; i < filter_size; ++i)
                    {
                        int w_index = f * filter_size + i;

                        //l->weights[w_index] = (double)l->weights[w_index] * l->scales[f] / (sqrt((double)l->rolling_variance[f]) + .000001f);
                        l->weights[w_index] = (double)l->weights[w_index] * l->scales[f] / (sqrt((double)l->rolling_variance[f] + .000001));
                    }
                }

                l->batch_normalize = 0;
#ifdef GPU

                if (gpu_index >= 0)
                {
                    push_convolutional_layer(*l);
                }

#endif
            }
        }
        else
        {
            //printf(" Fusion skip layer type: %d \n", l->type);
        }
    }
}

void forward_blank_layer(layer l, network_state state) {}

void calculate_binary_weights(network net)
{
    int j;

    for (j = 0; j < net.n; ++j)
    {
        layer *l = &net.layers[j];

        if (l->type == CONVOLUTIONAL)
        {
            //printf(" Merges Convolutional-%d and batch_norm \n", j);

            if (l->xnor)
            {
                //printf("\n %d \n", j);
                //l->lda_align = 256; // 256bit for AVX2    // set in make_convolutional_layer()
                //if (l->size*l->size*l->c >= 2048) l->lda_align = 512;

                binary_align_weights(l);

                if (net.layers[j].use_bin_output)
                {
                    l->activation = LINEAR;
                }

#ifdef GPU

                // fuse conv_xnor + shortcut -> conv_xnor
                if ((j + 1) < net.n && net.layers[j].type == CONVOLUTIONAL)
                {
                    layer *sc = &net.layers[j + 1];

                    if (sc->type == SHORTCUT && sc->w == sc->out_w && sc->h == sc->out_h && sc->c == sc->out_c)
                    {
                        l->bin_conv_shortcut_in_gpu = net.layers[net.layers[j + 1].index].output_gpu;
                        l->bin_conv_shortcut_out_gpu = net.layers[j + 1].output_gpu;

                        net.layers[j + 1].type = BLANK;
                        net.layers[j + 1].forward_gpu = forward_blank_layer;
                    }
                }

#endif  // GPU
            }
        }
    }

    //printf("\n calculate_binary_weights Done! \n");

}

void copy_cudnn_descriptors(layer src, layer *dst)
{
#ifdef CUDNN
    dst->normTensorDesc = src.normTensorDesc;
    dst->normDstTensorDesc = src.normDstTensorDesc;
    dst->normDstTensorDescF16 = src.normDstTensorDescF16;

    dst->srcTensorDesc = src.srcTensorDesc;
    dst->dstTensorDesc = src.dstTensorDesc;

    dst->srcTensorDesc16 = src.srcTensorDesc16;
    dst->dstTensorDesc16 = src.dstTensorDesc16;
#endif // CUDNN
}

void copy_weights_net(network net_train, network *net_map)
{
    int k;

    for (k = 0; k < net_train.n; ++k)
    {
        layer *l = &(net_train.layers[k]);
        layer tmp_layer;
        copy_cudnn_descriptors(net_map->layers[k], &tmp_layer);
        net_map->layers[k] = net_train.layers[k];
        copy_cudnn_descriptors(tmp_layer, &net_map->layers[k]);

        if (l->type == CRNN)
        {
            layer tmp_input_layer, tmp_self_layer, tmp_output_layer;
            copy_cudnn_descriptors(*net_map->layers[k].input_layer, &tmp_input_layer);
            copy_cudnn_descriptors(*net_map->layers[k].self_layer, &tmp_self_layer);
            copy_cudnn_descriptors(*net_map->layers[k].output_layer, &tmp_output_layer);
            net_map->layers[k].input_layer = net_train.layers[k].input_layer;
            net_map->layers[k].self_layer = net_train.layers[k].self_layer;
            net_map->layers[k].output_layer = net_train.layers[k].output_layer;
            //net_map->layers[k].output_gpu = net_map->layers[k].output_layer->output_gpu;  // already copied out of if()

            copy_cudnn_descriptors(tmp_input_layer, net_map->layers[k].input_layer);
            copy_cudnn_descriptors(tmp_self_layer, net_map->layers[k].self_layer);
            copy_cudnn_descriptors(tmp_output_layer, net_map->layers[k].output_layer);
        }

        net_map->layers[k].batch = 1;
        net_map->layers[k].steps = 1;
    }
}


// combine Training and Validation networks
network combine_train_valid_networks(network net_train, network net_map)
{
    network net_combined = make_network(net_train.n);
    layer *old_layers = net_combined.layers;
    net_combined = net_train;
    net_combined.layers = old_layers;
    net_combined.batch = 1;

    int k;

    for (k = 0; k < net_train.n; ++k)
    {
        layer *l = &(net_train.layers[k]);
        net_combined.layers[k] = net_train.layers[k];
        net_combined.layers[k].batch = 1;

        if (l->type == CONVOLUTIONAL)
        {
#ifdef CUDNN
            net_combined.layers[k].normTensorDesc = net_map.layers[k].normTensorDesc;
            net_combined.layers[k].normDstTensorDesc = net_map.layers[k].normDstTensorDesc;
            net_combined.layers[k].normDstTensorDescF16 = net_map.layers[k].normDstTensorDescF16;

            net_combined.layers[k].srcTensorDesc = net_map.layers[k].srcTensorDesc;
            net_combined.layers[k].dstTensorDesc = net_map.layers[k].dstTensorDesc;

            net_combined.layers[k].srcTensorDesc16 = net_map.layers[k].srcTensorDesc16;
            net_combined.layers[k].dstTensorDesc16 = net_map.layers[k].dstTensorDesc16;
#endif // CUDNN
        }
    }

    return net_combined;
}

void free_network_recurrent_state(network net)
{
    int k;

    for (k = 0; k < net.n; ++k)
    {
        if (net.layers[k].type == CONV_LSTM)
        {
            free_state_conv_lstm(net.layers[k]);
        }

        if (net.layers[k].type == CRNN)
        {
            free_state_crnn(net.layers[k]);
        }
    }
}

void randomize_network_recurrent_state(network net)
{
    int k;

    for (k = 0; k < net.n; ++k)
    {
        if (net.layers[k].type == CONV_LSTM)
        {
            randomize_state_conv_lstm(net.layers[k]);
        }

        if (net.layers[k].type == CRNN)
        {
            free_state_crnn(net.layers[k]);
        }
    }
}


void remember_network_recurrent_state(network net)
{
    int k;

    for (k = 0; k < net.n; ++k)
    {
        if (net.layers[k].type == CONV_LSTM)
        {
            remember_state_conv_lstm(net.layers[k]);
        }

        //if (net.layers[k].type == CRNN) free_state_crnn(net.layers[k]);
    }
}

void restore_network_recurrent_state(network net)
{
    int k;

    for (k = 0; k < net.n; ++k)
    {
        if (net.layers[k].type == CONV_LSTM)
        {
            restore_state_conv_lstm(net.layers[k]);
        }

        if (net.layers[k].type == CRNN)
        {
            free_state_crnn(net.layers[k]);
        }
    }
}